{"title":"Adaptive fault estimation for interconnected nonlinear system with triangular forms","authors":"Lei Liu, Zhanshan Wang","doi":"10.1109/ICICIP.2014.7010264","DOIUrl":null,"url":null,"abstract":"In this paper, a novel fault detection and estimation methodology is proposed for a class of interconnected nonlinear continues-time systems with triangular forms. In the distributed fault detection and estimation architecture, a local fault detector is utilized to generate a residual between the subsystem and its detector or observer. Moreover, a threshold for distributed fault detection and estimation in each subsystem are designed. Due to the universal approximation capabilities of the radial basis function neural networks, it is used to estimate the unknown fault dynamics. Finally, the proposed methods are verified in the simulation.","PeriodicalId":408041,"journal":{"name":"Fifth International Conference on Intelligent Control and Information Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Intelligent Control and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2014.7010264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a novel fault detection and estimation methodology is proposed for a class of interconnected nonlinear continues-time systems with triangular forms. In the distributed fault detection and estimation architecture, a local fault detector is utilized to generate a residual between the subsystem and its detector or observer. Moreover, a threshold for distributed fault detection and estimation in each subsystem are designed. Due to the universal approximation capabilities of the radial basis function neural networks, it is used to estimate the unknown fault dynamics. Finally, the proposed methods are verified in the simulation.